Platelet transfusion is critical for severely bleeding patients and nearly 7 million units are transfused in the United States and Europe annually. In the United States, platelets can only be stored for 5 days resulting in a waste of 15% of their supply. Short storage duration is a consequence of bacterial contamination and platelet quality considerations. Though many methods have been developed for bacterial testing and pathogen inactivation, fewer have been developed for improving quality of stored platelets. Platelet additive solutions have the possibility to increase storage quality and duration, reduce plasma-related allergic reactions, impact the efficacy of pathogen reduction techniques, and save plasma which can then be used as an additional transfusion product. While the benefits are well known, there has been little progress in developing new platelet additive solutions for increasing quality and safety of platelet transfusion because there is a lack of broad understanding of biochemical decline during storage. There has been interest to utilize high-throughput metabolite and protein profiling for global understanding of platelet metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic and signaling networks, and data-driven kinetic models to fully interpret and analyze platelet metabolite and protein profiles in a complete network context. The program will utilize recently generated time-course global, quantitative metabolite profiling to track intracellular and extracellular platelet metabolites under standard storage conditions and available proteomic studies in literature. The deep biochemical understanding obtained will be employed to quantitatively predict optimal additive solutions based on biological efficacy, cost, and regulator hurdles. Predicted additives will be chosen for experimental validation and testing in Phase II.